Matching products with service scenarios

ABSTRACT

For each service scenario out of a plurality of service scenarios, matching features of a to-be-matched product corresponding to the service scenario are acquired based on user features of users accessing the service scenario. A respective user feature mapping value of the service scenario is calculated based on the matching features of the to-be-matched product corresponding to the service scenario. Out of the plurality of service scenarios, a target service scenario of the to-be-matched product is selected based on the respective user feature mapping value of the service scenario.

This application is a continuation of U.S. patent application Ser. No.15/800,823, filed on Nov. 1, 2017, which is a continuation of PCTApplication No. PCT/CN2016/079811, filed on Apr. 21, 2016, which claimspriority to Chinese Patent Application No. 201510221616.6, filed on May4, 2015, the entire contents of each which are hereby incorporated byreference.

BACKGROUND

With Internet technologies, a user can visit or otherwise access variousweb pages provided by a web site service provider or an applicationservice provider through a web browser or an application (for example,an application on a mobile phone), or in other manners. A website or aservice provider can classify and collect different web pages to formdifferent service scenarios.

A service scenario can refer to a web page or a series of web pages thathave a common subject or function, for example, for providing aparticular service. A service scenario can include a series of web pagesthat are hierarchical in structure, and may also include one or morepop-up windows, conversational boxes, add-ons, or other components thatmay appear after a user's interaction with the service scenario (forexample, by clicking a button or icon on the main web page of theservice scenario).

As an example, a portal website can provide the following sub-sites:

SINA TECHNOLOGY

SINA SPORTS

SINA NEWS

SINA FINANCE

. . .

Each sub-site can be regarded as a service scenario. Each servicescenario can be further divided into service scenarios withincreasingly-focused subjects or functions for providing finer or morespecific services. For example, the SINA SPORTS sub-site( ) can includemultiple service scenarios, such as NBA, CBA, Chinese football, andinternational football. Similarly, the SINA TECHNOLOGY sub-site caninclude multiple service scenarios, such as mobile phones, cameras, andhousehold appliances.

SUMMARY

The present disclosure describes matching a product with a targetservice scenario.

In an implementation, for each service scenario out of a plurality ofservice scenarios, matching features of a to-be-matched productcorresponding to the service scenario are acquired based on userfeatures of users accessing the service scenario. A respective userfeature mapping value of the service scenario is calculated based on thematching features of the to-be-matched product corresponding to theservice scenario. Out of the plurality of service scenarios, a targetservice scenario of the to-be-matched product is selected based on therespective user feature mapping value of the service scenario.

Implementations of the described subject matter, including thepreviously described implementation, can be implemented using acomputer-implemented method; a non-transitory, computer-readable mediumstoring computer-readable instructions to perform thecomputer-implemented method; and a computer-implemented systemcomprising one or more computer memory devices interoperably coupledwith one or more computers and having tangible, non-transitory,machine-readable media storing instructions that, when executed by theone or more computers, perform the computer-implemented method/thecomputer-readable instructions stored on the non-transitory,computer-readable medium.

The subject matter described in this specification can be implemented inparticular implementations, so as to realize one or more of thefollowing advantages. First, the subject matter described can determinea target service scenario for a product based on multiple user features.Second, the subject matter described can provide a more appropriatetarget service scenario for a product. Other advantages will be apparentto those of ordinary skill in the art.

The details of one or more implementations of the subject matter of thisspecification are set forth in the Detailed Description, the Claims, andthe accompanying drawings. Other features, aspects, and advantages ofthe subject matter will become apparent from the Detailed Description,the Claims, and the accompanying drawings.

DESCRIPTION OF DRAWINGS

FIG. 1 is a table illustrating examples of multiple service scenarioswith their page views, according to an implementation of the presentdisclosure.

FIG. 2 is a flowchart illustrating an example of a computer-implementedmethod for matching a product with a target service scenario, accordingto an implementation of the present disclosure.

FIG. 3 is a block diagram of an example of a computer-implemented systemconfigured to matching a product with a target service scenario,according to an implementation of the present disclosure.

FIG. 4 is a block diagram of another example of a computer-implementedsystem configured to matching a product with a target service scenario,according to an implementation of the present disclosure.

FIG. 5 is a block diagram of another example of a computer-implementedsystem configured to matching a product with a target service scenario,according to an implementation of the present disclosure.

FIG. 6 is a block diagram illustrating an example of acomputer-implemented system used to provide computationalfunctionalities associated with described algorithms, methods,functions, processes, flows, and procedures, according to animplementation of the present disclosure.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

The following detailed description describes matching a product with atarget service scenario, and is presented to enable any person skilledin the art to make and use the disclosed subject matter in the contextof one or more particular implementations. Various modifications,alterations, and permutations of the disclosed implementations can bemade and will be readily apparent to those or ordinary skill in the art,and the general principles defined can be applied to otherimplementations and applications, without departing from the scope ofthe present disclosure. In some instances, details unnecessary to obtainan understanding of the described subject matter can be omitted so as tonot obscure one or more described implementations with unnecessarydetail and inasmuch as such details are within the skill of one ofordinary skill in the art. The present disclosure is not intended to belimited to the described or illustrated implementations, but to beaccorded the widest scope consistent with the described principles andfeatures.

A service scenario can include or correspond to one or more of differentaccess interfaces for different services. For example, access interfacesfor different types of services can be configured in mobile applications(for example, an application installed on a mobile terminal). As anexample, a home page of the ALIPAY Wallet can be configured to includeaccess interfaces for different types of services, such as, airplanetickets, films, taxis, and express delivery. In some implementations,similar to sub-sites, each of the access interfaces can be associatedwith additional access interfaces applicable to sub-services.

A website service provider or application service provider can configurea service scenario, for example, by configuring one or more accessinterfaces associated with the service scenario. As an example, aservice access interface for a new delivery service provider, XingxingExpress, can be configured on a page that is redirected from a web pagethat contains shipping options of merchandise in an applicationexecuting on a mobile terminal (for example, a smartphone or tablet-typecomputer). As another example, a website service provider may configure,in an automobile service scenario (for example, a website presentinginformation of automobiles of various bands), a service access interfaceof a new automobile brand. For example, the service access interface ofthe new automobile brand can include a pop-up window showing relatedservices (for example, a purchase consulting or a test driveappointment) of the new automobile brand in response to a click of abutton in the automobile service scenario. In some implementations,advertisements can be included in the service access interfaces. Thecontent of a service access interface (for example, the promotions,advertisements, or other information of the Xingxing Express and the newautomobile brand) can be referred to as a product.

In some implementations, a website service provider or applicationservice provider can determine a service scenario to which a product canbe placed, displayed, included or otherwise matched. In someimplementations, a product can be matched to a target service scenariobased on one or more criteria. The target service scenario can be, forexample, a service scenario that is most likely to attract users to viewor otherwise use the product, and further to buy or consume anunderlying good or service (for example, the purchase of the automobileor the delivery service) associated with the product.

The described techniques can help determine a more appropriate servicescenario for a product. Rather than merely using a single metric (forexample, page views of service scenarios), the described techniques canmatch a product with a target service scenario based on multiple userattributes or features (for example, registration information andbrowsing history of the users) with respect to different servicescenarios. The described techniques can help improve sales, marketpenetration, or user basis of the underlying service provider of theproduct. The described techniques can help improve a website serviceprovider or application service provider to better allocate, design, orotherwise configure its resources and contents to serve its customers(for example, the Xingxing Express and the new automobile brand who relyon the service scenario for advertisements and providing service accessinterfaces).

FIG. 1 is a table 100 illustrating examples of multiple servicescenarios 110 with their page views 120, according to an implementationof the present disclosure. The example service scenarios 110 includemultiple sub-sites of SINA, SINA BLOG 132, SINA NEWS 134, SINA FINANCE136, SINA TECHNOLOGY 138, and SINA SPORTS 140. Each of the exampleservice scenarios 110 has a respective page view value that includes thenumber of page views or hits by users in the last month. In someimplementations, the number of page views or hits of a web pageincreases by one every time a user refreshes the web page. For a servicescenario that includes multiple web pages, page views of the multiplepages in the service scenario can be counted as the page view of theservice scenario.

As shown in FIG. 1, SINA BLOG has the maximum page view. Conventionaltechniques may match the automobile product to the service scenario ofSINA BLOG based on the page view. Accordingly, a service accessinterface is set in the service scenario of SINA BLOG for the automobileproduct. As another example, a service access interface of XingxingExpress can be likewise matched to a taxi service scenario in anapplication based on page views of different service scenarios. In someimplementations, however, matching a product with a service scenarioonly based on a page view or the like may not give rise to the moreappropriate service scenario. The techniques described in thisdisclosure can help determine a more appropriate service scenario for aproduct, and allow configuration of a service access interfaceassociated with the product in the more appropriate service scenario.For example, the described techniques can match the automobile productwith the service scenario of SINA AUTOMOBILE, rather than SINA BLOG.Similarly, the described techniques can match the Xingxing Expressproduct with the service scenario of delivery service, rather than thetaxi service scenario.

FIG. 2 is a flowchart illustrating an example of a computer-implementedmethod 200 for matching a product with a target service scenario,according to an implementation of the present disclosure. For clarity ofpresentation, the description that follows generally describes method200 in the context of the other figures in this description. However, itwill be understood that method 200 can be performed, for example, by anysystem, environment, software, and hardware, or a combination ofsystems, environments, software, and hardware, as appropriate. In someimplementations, various steps of method 200 can be run in parallel, incombination, in loops, or in any order.

At 202, for each service scenario out of a number of service scenarios,one or more matching features of a to-be-matched product correspondingto the service scenario are acquired based on user features of usersaccessing the service scenario. In some implementations, the servicescenario comprises one or more web page accessible by the users, forexample, through a web browser or an application (for example, anapplication on a mobile phone), or in another manner. The to-be-matchedproduct comprises content (for example, promotions, advertisements,product or service descriptions, contact, or other information) to bepresented in a target service scenario of the to-be-matched product.

In some implementations, the one or more matching features are acquiredby data processing apparatus (for example, a server of a website serviceprovider or application service provider). In some implementations,acquiring matching features of a to-be-matched product can includeretrieving or receiving the one or more matching features locally from amemory or a data store, or remotely from another device over acommunication network. In some implementations, acquiring matchingfeatures of a to-be-matched product can include identifying selecting,or deriving attributes or features that can be used to determine thetarget service scenario of the to-be-matched product based on the userfeatures of users accessing the service scenario.

In some implementations, a user visits or otherwise accesses, through aterminal (for example, a web browser) or an application (for example, amobile app on a mobile device), different service scenarios that areprovided by a website service provider or an application serviceprovider. In some implementations, the server of the website serviceprovider or the application service provider can receive, record, log,or otherwise acquire registration information, access records, or otherinformation of the user that accesses the service scenarios. In someimplementations, the registration information, access records, or otherinformation of the user can be retrieved, received, bought, or otherwiseacquired from a database, a survey agency, or some other sources.

In some implementations, the user features of users accessing theservice scenario can include features derived from registrationinformation or an access record of the users accessing the servicescenario. For example, the user features can include features derivedfrom registration information, such as, the user's identifier (ID),gender, age, and address of the user. In some implementations, theaccess record can includes subscription information, and browse historyof the user. For example, the user features can include features derivedfrom the subscription information (for example, subscription informationspecified or other identified by the user). Such user features caninclude, for example, one or more of the user's registered hobbies orinterests, subscribed sub-site or sections, favorite or bookmarked webpages, or other information indicates the user's interests, preferences,or habits. In some implementations, the user features can also includefeatures derived from recorded historical behaviors of the user withrespect to the website or the application. Such user features caninclude, for example, total amounts of the user's transactions (forexample, purchase, sale, trade, or other financial activities) indifferent categories (for example, maternal and infant goods, householdappliance, and outdoor products) on a shopping website or anapplication.

In some implementations, the data processing apparatus can receive,aggregate, derive, or otherwise acquire different user features of usersaccessing different service scenarios provided by one or more of awebsite service provider or an application service provider. The userfeatures can be used for evaluating user feature mapping values ofdifferent service scenarios.

As an example, user features can be in the form of {X1, X2, X3, . . . ,X_(n)}, wherein X1, X2, X3, . . . , X_(n) represent some or all of thefollowing information of the user: {ID, gender, age, subscribed orfollowed service scenarios, hobbies or interests, subscribed sub-sitesor sections, favorite or bookmarked web pages, purchase history, totaltransaction amount in maternal and infant goods, total transactionamount in household appliance, total transaction amount in outdoorsclass products, . . . }. The user features can represent additional ordifferent information of users accessing the service scenario. Differentusers may have the same or different user features.

Different service scenarios may attract respective groups of users tovisit. For example, a service scenario 1 may include users M1, M2, M3, .. . , M100; a service scenario 2 may include users N1, N2, N3, . . . ,N50; a service scenario 3 may include users P1, P2, P3, . . . , P500;and a service scenario 4 may include users Q1, Q2, Q3, . . . , Q300. Insome implementations, the group users accessing different servicescenarios can be distinct, partially overlapped, or fully overlapped.The respective groups of users accessing the different service scenariosmay share identical, similar, or different user features. In someimplementations, the common or similar user features of a certain groupof users accessing a certain service scenario may correlate with auser's likelihood of accessing the certain service scenario.Accordingly, some of the user features can be used as matching featuresfor matching a to-be-matched product with a target service scenario.

Matching features of the to-be-matched product corresponding to theservice scenario can be selected, derived, or otherwise acquired basedon the user features of users accessing the service scenario. In someimplementations, acquiring matching features of a to-be-matched productcorresponding to the service scenario based on user features of usersaccessing the service scenario comprises acquiring a predeterminedselection of features that are acquired based on the user features ofusers accessing the service scenario. In some implementations, thepredetermined selection of matching features that can include apredetermined selection of user features themselves, or a predeterminedselection of features that are derived from the user features (forexample, with quantifying, normalization, or other operations).

For example, in matching a maternal and child product, such as maternaland child class advertising or application interface, the matchingfeatures can include a predetermined selection of user features such asthe gender, age, a service scenario of interest, hobbies, favorite orbookmarked page, purchase history, and maternal and child class totaltransaction amount. As another example, for an automotive products, suchas a consulting interface for automobile purchases, the matchingfeatures can include another predetermined selection of user featuressuch as user's hobbies, interests, subscriptions, favorite or bookmarkpages, and purchase records. Different products can have the same ordifferent matching features.

In some implementations, at 202, in addition to or as an alternative tousing a predetermined selection of features, the matching features canbe acquired automatically, for example, based on the user features ofusers accessing the service scenario according to one or more dataprocessing techniques for processing big data. For example, one or moremachine learning algorithms, including but not limited to, examplelogistic regression, Gradient Boosting Decision Tree (GBDT), decisiontrees, and even depth learning machine learning methods can be used toacquire the matching features.

In some implementations, with the big data processing method,relationships (for example, in terms of functions and weights) betweenthe matching features and the user features can be acquired. In someimplementations, a unified equation or formula can be acquired. In thisdisclosure, logistic regression is used as an example of describing howto acquire the matching features based on modeling by matching features,and how to acquire a user feature mapping value of the service scenariobased on the matching features, where the value falls within the rangebetween 0 and 1. Additional or different big data processing algorithmscan be used.

In some implementations, the matching features can be acquired byselecting key features using logistic regression (also known asprincipal component analysis in machine learning). The key featuresinclude features to be used in determining a user feature mapping valueof the service scenario. In some implementations, acquiring the matchingfeatures according to a machine learning algorithm can include one ormore of the following steps.

A1: annotate all user features of a user accessing a service scenariobased on whether the user uses the target product. For example, for auser data that includes 500 users, each user having 1000 user features,an annotation or label can be added to each user, to indicate whetherthe user uses the target product. The annotation or label can be abinary value (for example, 1 means that the user uses the targetproduct, while 0 means that the user does not use the target product.)or in another format.

A2: calculate, for each user feature (out of the 1000 user features), aninformation value between the each user feature and the annotation, forexample, according to principal component analysis. As a result, adegree of influence of each of the user feature on whether the user usesthe target product can be calculated. The information value can bedetermined based on the degree of influence.

A3: assort the user features according to the information value. Apredetermined number of user features can be kept as the key feature. Inthe example, the 1000 user features can be sorted according to therespective information values, and the 200 user features with thelargest 200 information values can be kept as the key feature.

A4: perform a logistic regression using the preset number of userfeatures and whether the user uses the target product. Based on thestatistical significance requirement (p_value) of the logisticregression algorithm, exclude non-significant indicators and retainsignificant indicators.

In this way, the matching features of the target product can be acquiredby determining a smaller number of user features that are most relevantin determining whether the user uses the target product. From 202,method 200 proceeds to 204.

At 204, a respective user feature mapping value of the service scenariois calculated based on the matching features of the to-be-matchedproduct corresponding to the service scenario. In some implementations,calculating a respective user feature mapping value of the servicescenario based on the matching features of the to-be-matched productcorresponding to the service scenario comprises quantifying a first userfeature corresponding to a first matching feature of the to-be-matchedproduct corresponding to the service scenario according to aquantification rule; and calculating the respective user feature mappingvalue of the service scenario based on the first user featurecorresponding to the first matching feature respective user featureaccording to a mapping rule. The quantification rule can, for example,define a range (for example, from 0 to 1) of resulting quantified firstuser feature corresponding to a first matching feature. In someimplementations, the quantification rule can be the same for calculatingrespective user feature mapping values of different service scenarios.The mapping rule can include one or more functions of the matchingfeatures of the to-be-matched product. The one or more functions caninclude, for example, summation, averaging, or other operations. Themapping rule can be the same for calculating respective user featuremapping values of different service scenarios. In other words, the samemapping rule and the same quantification rule can be applied forcalculating respective user feature mapping values across all candidateservice scenarios, so as to ensure the uniformity among all candidateservice scenarios and to more accurately reflect the correlation betweenthe to-be-matched product and a certain service scenario.

As example, assume that matching features of a to-be-matched productinclude {X2, X3, X5, X8}. For each of the different service scenarios, arespective user feature mapping value can be calculated based on theuser features corresponding to the matching features of theto-be-matched product. For example, for each user in the servicescenario 1, the user features corresponding to the matching features{X2, X3, X5, X8} can be quantified according to a quantification rule,and the quantified user features corresponding to the service scenario 1can be summed and averaged to serve as a user feature mapping value forthe service scenario 1 (denoted as MV_(service scenario 1)). For eachuser in the service scenario 2, the user features corresponding to thematching features {X2, X3, X5, X8} can be quantified according to thesame quantification rule, and the quantified user features correspondingto the service scenario 2 can be summed and averaged to serve as a userfeature mapping value for the service scenario 2 (denoted asMV_(service scenario 2)). As an example, the resulting user featuremapping values for the two service scenarios are:MV_(service scenario 1)=1.5; MV_(service scenario 2)=2.7.

Similarly, for each user in the service scenario 3, the user featurescorresponding to the matching features {X2, X3, X5, X8} can bequantified according to a quantification rule, and the quantified userfeatures corresponding to the service scenario 3 can be summed andaveraged to serve as a user feature mapping value for the servicescenario 1 (denoted as MV_(service scenario 3)). For each user in theservice scenario 4, the user features corresponding to the matchingfeatures {X2, X3, X5, X8} can be quantified according to the samequantification rule, and the quantified user features corresponding tothe service scenario 4 can be summed and averaged to serve as a userfeature mapping value for the service scenario 4 (denoted asMV_(service scenario 4)). As an example, the resulting user featuremapping values for the two service scenarios are:MV_(service scenario 3)=15000; MV_(service scenario 4)=7280.

In some implementations, calculating a respective user feature mappingvalue of the service scenario based on the matching features of theto-be-matched product corresponding to the service scenario comprisesobtaining a mapping function between the respective user feature mappingvalue of the service scenario and the matching features of theto-be-matched product corresponding to the service scenario using amachine learning method; and calculating the respective user featuremapping value of the service scenario based on the mapping function.

For example, by using the machine learning algorithm at 202 foracquiring matching features of the to-be-matched product, at A4, amapping relationship between the matching feature and the user featuremapping value can be acquired by performing the logistic regression,excluding non-significant indicators, and keeping significant indicatorsand its corresponding coefficients, based on the statisticalsignificance requirement (p_value). The mapping relationship can be amapping relationship between every matching features to the user featuremapping value with a final range of [0,1]. As such, at 204, therespective user feature mapping value of the service scenario can becalculated based on using the mapping relationship for each servicescenario based on the matching features of the to-be-matched products tobe matched user feature mapping value. In some implementations, becauseof the mapping derived by the logistic regression algorithm are based onthe collection, trial and trend simulation of big data, it may betterreflect the relationship between the user features and the matchingfeatures, and the relationship between the matching features and theuser feature map values. From 204, method 200 proceeds to 206.

At 206, a target service scenario of the to-be-matched product isselected, out of the number of service scenarios, based on therespective user feature mapping value of the service scenario. In someimplementations, the more matching features of the to-be-matched productthat a service scenario have, the better match that the service scenariowould be for the to-be-matched product. Thus, high user feature mappingvalue may be selected to be the target service scenario matching theto-be-matched product. In the previous example, the service scenario 3has more users and thus more user features corresponding to the matchingfeatures of the to-be-matched product. As such, the user feature mappingvalues for the service scenario 3 is larger than that of the servicescenario 4, making the service scenario 3 a better match for theto-be-matched product than the service scenario 4. In someimplementations, a service scenario having a highest user featuremapping value among a number of respective user feature mapping valuesof the number of service scenarios can be selected as the target servicescenario of the to-be-matched product. In some implementations, morethan one service scenario can be selected as the target service scenarioof the to-be-matched product.

In some implementations, a certain matching feature of the to-be-matchedproduct that is repeated more often than other features in a certainservice scenario, may make the certain service scenario a better matchto to-be-matched product. For example, assume the to-be-matched productis maternal and child product. In some implementations, users whopurchase the maternal and child product are mainly concentrated in theservice scenario 2. Accordingly, some matching features (for example,total amount of transactions) corresponding to the user accessing theservice scenario 2 may have a higher impact on the user feature mappingvalue of the service scenario. As such, even if there are significantlyless users accessing service scenario 1 than accessing service scenario1, according to the calculation described at 204, the user featuremapping values for the service scenario 2 is still higher than that ofthe service scenario 1, suggesting the service scenario 2 is a betterservice scenario to place the product than the service scenario 1.

In some implementations, depending on the nature of the to-be-matchedproduct, a service scenario that includes less matching features of theto-be-matched product may be a better fit for the to-be-matched product.Thus, a service scenario with a low user feature mapping value may beselected to be the service scenario matching products business servicescenarios. In some implementations, a service scenario having a lowestuser feature mapping value among a number of respective user featuremapping values of the number of service scenarios is selected as thetarget service scenario of the to-be-matched product. Additional ordifferent selection choices based on the user feature mapping value canbe made. After 206, method 200 stops.

FIG. 3 is a block diagram of an example of a computer-implemented system300 configured to matching a product with a target service scenario,according to an implementation of the present disclosure.

As shown in FIG. 3, the system includes an acquisition unit 310configured to for each service scenario out of a number of servicescenarios, matching features of a to-be-matched product corresponding tothe service scenario based on user features of users accessing theservice scenario; a calculation unit 320 configured to calculate arespective user feature mapping value of the service scenario based onthe matching features of the to-be-matched product corresponding to theservice scenario; and a selection unit 330 configured to select, out ofthe number of service scenarios, a target service scenario of theto-be-matched product based on the respective user feature mapping valueof the service scenario.

In some implementations, the acquisition unit 310 may include a firstacquisition unit configured to acquire the user features of usersaccessing the service scenario that comprises features derived fromregistration information or an access record of the users accessing theservice scenario. In some implementations, the acquisition unit 310 mayinclude a second acquisition unit configured to acquire a predeterminedselection of features based on the user features of users accessing theservice scenario as the matching features of the to-be-matched product.

FIG. 4 is a block diagram of an example of another computer-implementedsystem 400 configured to matching a product with a target servicescenario, according to an implementation of the present disclosure. Insome implementations, as shown in FIG. 4, the calculation unit 320 mayinclude a quantification unit 321 and a mapping unit 322, wherein thequantification unit 321 is configured to quantify a first user featurecorresponding to a first matching feature of the to-be-matched productcorresponding to the service scenario according to a quantificationrule, wherein the quantification rule is the same for the number ofservice scenarios; and the mapping unit 322 is configured to calculatethe respective user feature mapping value of the service scenario basedon the first user feature corresponding to the first matching featurerespective user feature according to a mapping rule, wherein the mappingrule is the same for the number of service scenarios.

In some implementations, the selection unit may select a servicescenario having a highest user feature mapping value among a number ofrespective user feature mapping values of the number of servicescenarios as the target service scenario of the to-be-matched product;or a service scenario having a lowest user feature mapping value among anumber of respective user feature mapping values of the number ofservice scenarios as the target service scenario of the to-be-matchedproduct.

In some implementations, the acquiring matching features of theto-be-matched product by the acquisition unit can be acquiring matchingfeatures of a to-be-matched product corresponding to the servicescenario based on the user features of users accessing the servicescenario according to a machine learning algorithm.

FIG. 5 is a block diagram of an example of another computer-implementedsystem 500 configured to matching a product with a target servicescenario, according to an implementation of the present disclosure. Insome implementations, as shown in FIG. 5, the mapping unit 322 mayinclude a mapping manner obtaining unit 323 and a feature valuecalculation unit 324, wherein the mapping manner obtaining unit 323 isconfigured to obtain a mapping function between the respective userfeature mapping value of the service scenario and the matching featuresof the to-be-matched product corresponding to the service scenario usinga machine learning method; and the feature value calculation unit 324 isconfigured to calculate the respective user feature mapping value of theservice scenario based on the mapping function.

In some implementations, the machine learning method may include atleast one of a logistic regression algorithm, a GBDT algorithm, adecision tree algorithm, and a deep learning algorithm.

FIG. 6 is a block diagram illustrating an example of acomputer-implemented system 600 used to provide computationalfunctionalities associated with described algorithms, methods,functions, processes, flows, and procedures, according to animplementation of the present disclosure. In the illustratedimplementation, system 600 includes a computer 602 and a network 630.

The illustrated computer 602 is intended to encompass any computingdevice such as a server, desktop computer, laptop/notebook computer,wireless data port, smart phone, personal data assistant (PDA), tabletcomputing device, one or more processors within these devices, anothercomputing device, or a combination of computing devices, includingphysical or virtual instances of the computing device, or a combinationof physical or virtual instances of the computing device. Additionally,the computer 602 can include an input device, such as a keypad,keyboard, touch screen, another input device, or a combination of inputdevices that can accept user information, and an output device thatconveys information associated with the operation of the computer 602,including digital data, visual, audio, another type of information, or acombination of types of information, on a graphical-type user interface(UI) (or GUI) or other UI.

The computer 602 can serve in a role in a distributed computing systemas a client, network component, a server, a database or anotherpersistency, another role, or a combination of roles for performing thesubject matter described in the present disclosure. The illustratedcomputer 602 is communicably coupled with a network 630. In someimplementations, one or more components of the computer 602 can beconfigured to operate within an environment, includingcloud-computing-based, local, global, another environment, or acombination of environments.

At a high level, the computer 602 is an electronic computing deviceoperable to receive, transmit, process, store, or manage data andinformation associated with the described subject matter. According tosome implementations, the computer 602 can also include or becommunicably coupled with a server, including an application server,e-mail server, web server, caching server, streaming data server,another server, or a combination of servers.

The computer 602 can receive requests over network 630 (for example,from a client software application executing on another computer 602)and respond to the received requests by processing the received requestsusing a software application or a combination of software applications.In addition, requests can also be sent to the computer 602 from internalusers (for example, from a command console or by another internal accessmethod), external or third-parties, or other entities, individuals,systems, or computers.

Each of the components of the computer 602 can communicate using asystem bus 603. In some implementations, any or all of the components ofthe computer 602, including hardware, software, or a combination ofhardware and software, can interface over the system bus 603 using anapplication programming interface (API) 612, a service layer 613, or acombination of the API 612 and service layer 613. The API 612 caninclude specifications for routines, data structures, and objectclasses. The API 612 can be either computer-language independent ordependent and refer to a complete interface, a single function, or evena set of APIs. The service layer 613 provides software services to thecomputer 602 or other components (whether illustrated or not) that arecommunicably coupled to the computer 602. The functionality of thecomputer 602 can be accessible for all service consumers using thisservice layer. Software services, such as those provided by the servicelayer 613, provide reusable, defined functionalities through a definedinterface. For example, the interface can be software written in JAVA,C++, another computing language, or a combination of computing languagesproviding data in extensible markup language (XML) format, anotherformat, or a combination of formats. While illustrated as an integratedcomponent of the computer 602, alternative implementations canillustrate the API 612 or the service layer 613 as stand-alonecomponents in relation to other components of the computer 602 or othercomponents (whether illustrated or not) that are communicably coupled tothe computer 602. Moreover, any or all parts of the API 612 or theservice layer 613 can be implemented as a child or a sub-module ofanother software module, enterprise application, or hardware modulewithout departing from the scope of the present disclosure.

The computer 602 includes an interface 604. Although illustrated as asingle interface 604 in FIG. 6, two or more interfaces 604 can be usedaccording to particular needs, desires, or particular implementations ofthe computer 602. The interface 604 is used by the computer 602 forcommunicating with another computing system (whether illustrated or not)that is communicatively linked to the network 630 in a distributedenvironment. Generally, the interface 604 is operable to communicatewith the network 630 and includes logic encoded in software, hardware,or a combination of software and hardware. More specifically, theinterface 604 can include software supporting one or more communicationprotocols associated with communications such that the network 630 orinterface's hardware is operable to communicate physical signals withinand outside of the illustrated computer 602.

The computer 602 includes a processor 605. Although illustrated as asingle processor 605 in FIG. 6, two or more processors can be usedaccording to particular needs, desires, or particular implementations ofthe computer 602. Generally, the processor 605 executes instructions andmanipulates data to perform the operations of the computer 602 and anyalgorithms, methods, functions, processes, flows, and procedures asdescribed in the present disclosure.

The computer 602 also includes a database 606 that can hold data for thecomputer 602, another component communicatively linked to the network630 (whether illustrated or not), or a combination of the computer 602and another component. For example, database 606 can be an in-memory,conventional, or another type of database storing data consistent withthe present disclosure. In some implementations, database 606 can be acombination of two or more different database types (for example, ahybrid in-memory and conventional database) according to particularneeds, desires, or particular implementations of the computer 602 andthe described functionality. Although illustrated as a single database606 in FIG. 6, two or more databases of similar or differing types canbe used according to particular needs, desires, or particularimplementations of the computer 602 and the described functionality.While database 606 is illustrated as an integral component of thecomputer 602, in alternative implementations, database 606 can beexternal to the computer 602. As illustrated, the database 606 holds thepreviously described user features 616 of users accessing one or moreservice scenarios and matching features 618 of a to-be-matched product.

The computer 602 also includes a memory 607 that can hold data for thecomputer 602, another component or components communicatively linked tothe network 630 (whether illustrated or not), or a combination of thecomputer 602 and another component. Memory 607 can store any dataconsistent with the present disclosure. In some implementations, memory607 can be a combination of two or more different types of memory (forexample, a combination of semiconductor and magnetic storage) accordingto particular needs, desires, or particular implementations of thecomputer 602 and the described functionality. Although illustrated as asingle memory 607 in FIG. 6, two or more memories 607 or similar ordiffering types can be used according to particular needs, desires, orparticular implementations of the computer 602 and the describedfunctionality. While memory 607 is illustrated as an integral componentof the computer 602, in alternative implementations, memory 607 can beexternal to the computer 602.

The application 608 is an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 602, particularly with respect tofunctionality described in the present disclosure. For example,application 608 can serve as one or more components, modules, orapplications. Further, although illustrated as a single application 608,the application 608 can be implemented as multiple applications 608 onthe computer 602. In addition, although illustrated as integral to thecomputer 602, in alternative implementations, the application 608 can beexternal to the computer 602.

The computer 602 can also include a power supply 614. The power supply614 can include a rechargeable or non-rechargeable battery that can beconfigured to be either user- or non-user-replaceable. In someimplementations, the power supply 614 can include power-conversion ormanagement circuits (including recharging, standby, or another powermanagement functionality). In some implementations, the power-supply 614can include a power plug to allow the computer 602 to be plugged into awall socket or another power source to, for example, power the computer602 or recharge a rechargeable battery.

There can be any number of computers 602 associated with, or externalto, a computer system containing computer 602, each computer 602communicating over network 630. Further, the term “client,” “user,” orother appropriate terminology can be used interchangeably, asappropriate, without departing from the scope of the present disclosure.Moreover, the present disclosure contemplates that many users can useone computer 602, or that one user can use multiple computers 602.

Described implementations of the subject matter can include one or morefeatures, alone or in combination.

For example, in a first implementation, a computer-implemented method,comprising: acquiring, by data processing apparatus, for each servicescenario out of a plurality of service scenarios, matching features of ato-be-matched product corresponding to the service scenario based onuser features of users accessing the service scenario; calculating, bythe data processing apparatus, a respective user feature mapping valueof the service scenario based on the matching features of theto-be-matched product corresponding to the service scenario; andselecting, by the data processing apparatus, out of the plurality ofservice scenarios, a target service scenario of the to-be-matchedproduct based on the respective user feature mapping value of theservice scenario.

In a second implement, a non-transitory, computer-readable mediumstoring one or more instructions executable by a computer system toperform operations comprising: acquiring, for each service scenario outof a plurality of service scenarios, matching features of ato-be-matched product corresponding to the service scenario based onuser features of users accessing the service scenario; calculating arespective user feature mapping value of the service scenario based onthe matching features of the to-be-matched product corresponding to theservice scenario; and selecting, out of the plurality of servicescenarios, a target service scenario of the to-be-matched product basedon the respective user feature mapping value of the service scenario.

In a third implementation, a computer-implemented system, comprising:one or more computers; and one or more computer memory devicesinteroperably coupled with the one or more computers and havingtangible, non-transitory, machine-readable media storing one or moreinstructions that, when executed by the one or more computers, performone or more operations comprising: acquiring, by data processingapparatus, for each service scenario out of a plurality of servicescenarios, matching features of a to-be-matched product corresponding tothe service scenario based on user features of users accessing theservice scenario; calculating, by the data processing apparatus, arespective user feature mapping value of the service scenario based onthe matching features of the to-be-matched product corresponding to theservice scenario; and selecting, by the data processing apparatus, outof the plurality of service scenarios, a target service scenario of theto-be-matched product based on the respective user feature mapping valueof the service scenario.

The foregoing and other described implementations can each, optionally,include one or more of the following features:

A first feature, combinable with any of the following features, whereinthe user features of users accessing the service scenario comprisesfeatures derived from registration information or an access record ofthe users accessing the service scenario.

A second feature, combinable with any of the previous or followingfeatures, wherein acquiring matching features of a to-be-matched productcorresponding to the service scenario based on user features of usersaccessing the service scenario comprises acquiring a predeterminedselection of features based on the user features of users accessing theservice scenario as the matching features of the to-be-matched product.

A third feature, combinable with any of the previous or followingfeatures, wherein calculating a respective user feature mapping value ofthe service scenario based on the matching features of the to-be-matchedproduct corresponding to the service scenario comprises: quantifying afirst user feature corresponding to a first matching feature of theto-be-matched product corresponding to the service scenario according toa quantification rule, wherein the quantification rule is the same forthe plurality of service scenarios; and calculating the respective userfeature mapping value of the service scenario based on the first userfeature corresponding to the first matching feature respective userfeature according to a mapping rule, wherein the mapping rule is thesame for the plurality of service scenarios.

A fourth feature, combinable with any of the previous or followingfeatures, wherein selecting, out of the plurality of service scenarios,a target service scenario of the to-be-matched product based on therespective user feature mapping value of the service scenario comprises:selecting a service scenario having a lowest user feature mapping valueamong a plurality of respective user feature mapping values of theplurality of service scenarios as the target service scenario of theto-be-matched product; or selecting a service scenario having a highestuser feature mapping value among a plurality of respective user featuremapping values of the plurality of service scenarios as the targetservice scenario of the to-be-matched product.

A fifth feature, combinable with any of the previous or followingfeatures, wherein acquiring matching features of a to-be-matched productcorresponding to the service scenario based on user features of usersaccessing the service scenario comprises acquiring matching features ofa to-be-matched product corresponding to the service scenario based onthe user features of users accessing the service scenario according to amachine learning algorithm.

A sixth feature, combinable with any of the previous or followingfeatures, wherein the machine learning algorithm comprises at least oneor more of a logistic regression algorithm, a Gradient Boosting DecisionTree (GBDT) algorithm, a decision tree algorithm, or a deep learningalgorithm.

A seventh feature, combinable with any of the previous or followingfeatures, wherein calculating a respective user feature mapping value ofthe service scenario based on the matching features of the to-be-matchedproduct corresponding to the service scenario comprises: obtaining amapping function between the respective user feature mapping value ofthe service scenario and the matching features of the to-be-matchedproduct corresponding to the service scenario using a machine learningmethod; and calculating the respective user feature mapping value of theservice scenario based on the mapping function.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Software implementations of the described subjectmatter can be implemented as one or more computer programs, that is, oneor more modules of computer program instructions encoded on a tangible,non-transitory, computer-readable computer-storage medium for executionby, or to control the operation of, data processing apparatus.Alternatively, or additionally, the program instructions can be encodedin/on an artificially generated propagated signal, for example, amachine-generated electrical, optical, or electromagnetic signal that isgenerated to encode information for transmission to a receiver apparatusfor execution by a data processing apparatus. The computer-storagemedium can be a machine-readable storage device, a machine-readablestorage substrate, a random or serial access memory device, or acombination of computer-storage mediums. Configuring one or morecomputers means that the one or more computers have installed hardware,firmware, or software (or combinations of hardware, firmware, andsoftware) so that when the software is executed by the one or morecomputers, particular computing operations are performed.

The term “real-time,” “real time,” “realtime,” “real (fast) time (RFT),”“near(ly) real-time (NRT),” “quasi real-time,” or similar terms (asunderstood by one of ordinary skill in the art), means that an actionand a response are temporally proximate such that an individualperceives the action and the response occurring substantiallysimultaneously. For example, the time difference for a response todisplay (or for an initiation of a display) of data following theindividual's action to access the data can be less than 1 millisecond(ms), less than 1 second (s), or less than 5 s. While the requested dataneed not be displayed (or initiated for display) instantaneously, it isdisplayed (or initiated for display) without any intentional delay,taking into account processing limitations of a described computingsystem and time required to, for example, gather, accurately measure,analyze, process, store, or transmit the data.

The terms “data processing apparatus,” “computer,” or “electroniccomputer device” (or equivalent as understood by one of ordinary skillin the art) refer to data processing hardware and encompass all kinds ofapparatus, devices, and machines for processing data, including by wayof example, a programmable processor, a computer, or multiple processorsor computers. The apparatus can also be, or further include specialpurpose logic circuitry, for example, a central processing unit (CPU),an FPGA (field programmable gate array), or an ASIC(application-specific integrated circuit). In some implementations, thedata processing apparatus or special purpose logic circuitry (or acombination of the data processing apparatus or special purpose logiccircuitry) can be hardware- or software-based (or a combination of bothhardware- and software-based). The apparatus can optionally include codethat creates an execution environment for computer programs, forexample, code that constitutes processor firmware, a protocol stack, adatabase management system, an operating system, or a combination ofexecution environments. The present disclosure contemplates the use ofdata processing apparatuses with an operating system of some type, forexample LINUX, UNIX, WINDOWS, MAC OS, ANDROID, IOS, another operatingsystem, or a combination of operating systems.

A computer program, which can also be referred to or described as aprogram, software, a software application, a unit, a module, a softwaremodule, a script, code, or other component can be written in any form ofprogramming language, including compiled or interpreted languages, ordeclarative or procedural languages, and it can be deployed in any form,including, for example, as a stand-alone program, module, component, orsubroutine, for use in a computing environment. A computer program can,but need not, correspond to a file in a file system. A program can bestored in a portion of a file that holds other programs or data, forexample, one or more scripts stored in a markup language document, in asingle file dedicated to the program in question, or in multiplecoordinated files, for example, files that store one or more modules,sub-programs, or portions of code. A computer program can be deployed tobe executed on one computer or on multiple computers that are located atone site or distributed across multiple sites and interconnected by acommunication network.

While portions of the programs illustrated in the various figures can beillustrated as individual components, such as units or modules, thatimplement described features and functionality using various objects,methods, or other processes, the programs can instead include a numberof sub-units, sub-modules, third-party services, components, libraries,and other components, as appropriate. Conversely, the features andfunctionality of various components can be combined into singlecomponents, as appropriate. Thresholds used to make computationaldeterminations can be statically, dynamically, or both statically anddynamically determined.

Described methods, processes, or logic flows represent one or moreexamples of functionality consistent with the present disclosure and arenot intended to limit the disclosure to the described or illustratedimplementations, but to be accorded the widest scope consistent withdescribed principles and features. The described methods, processes, orlogic flows can be performed by one or more programmable computersexecuting one or more computer programs to perform functions byoperating on input data and generating output data. The methods,processes, or logic flows can also be performed by, and apparatus canalso be implemented as, special purpose logic circuitry, for example, aCPU, an FPGA, or an ASIC.

Computers for the execution of a computer program can be based ongeneral or special purpose microprocessors, both, or another type ofCPU. Generally, a CPU will receive instructions and data from and writeto a memory. The essential elements of a computer are a CPU, forperforming or executing instructions, and one or more memory devices forstoring instructions and data. Generally, a computer will also include,or be operatively coupled to, receive data from or transfer data to, orboth, one or more mass storage devices for storing data, for example,magnetic, magneto-optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, for example, a mobile telephone, a personal digitalassistant (PDA), a mobile audio or video player, a game console, aglobal positioning system (GPS) receiver, or a portable memory storagedevice.

Non-transitory computer-readable media for storing computer programinstructions and data can include all forms of permanent/non-permanentor volatile/non-volatile memory, media and memory devices, including byway of example semiconductor memory devices, for example, random accessmemory (RAM), read-only memory (ROM), phase change memory (PRAM), staticrandom access memory (SRAM), dynamic random access memory (DRAM),erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and flash memory devices;magnetic devices, for example, tape, cartridges, cassettes,internal/removable disks; magneto-optical disks; and optical memorydevices, for example, digital video disc (DVD), CD-ROM, DVD+/−R,DVD-RAM, DVD-ROM, HD-DVD, and BLURAY, and other optical memorytechnologies. The memory can store various objects or data, includingcaches, classes, frameworks, applications, modules, backup data, jobs,web pages, web page templates, data structures, database tables,repositories storing dynamic information, or other appropriateinformation including any parameters, variables, algorithms,instructions, rules, constraints, or references. Additionally, thememory can include other appropriate data, such as logs, policies,security or access data, or reporting files. The processor and thememory can be supplemented by, or incorporated in, special purpose logiccircuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, for example, a CRT (cathode ray tube), LCD(liquid crystal display), LED (Light Emitting Diode), or plasma monitor,for displaying information to the user and a keyboard and a pointingdevice, for example, a mouse, trackball, or trackpad by which the usercan provide input to the computer. Input can also be provided to thecomputer using a touchscreen, such as a tablet computer surface withpressure sensitivity, a multi-touch screen using capacitive or electricsensing, or another type of touchscreen. Other types of devices can beused to interact with the user. For example, feedback provided to theuser can be any form of sensory feedback (such as, visual, auditory,tactile, or a combination of feedback types). Input from the user can bereceived in any form, including acoustic, speech, or tactile input. Inaddition, a computer can interact with the user by sending documents toand receiving documents from a client computing device that is used bythe user (for example, by sending web pages to a web browser on a user'smobile computing device in response to requests received from the webbrowser).

The term “graphical user interface,” or “GUI,” can be used in thesingular or the plural to describe one or more graphical user interfacesand each of the displays of a particular graphical user interface.Therefore, a GUI can represent any graphical user interface, includingbut not limited to, a web browser, a touch screen, or a command lineinterface (CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI can include a numberof user interface (UI) elements, some or all associated with a webbrowser, such as interactive fields, pull-down lists, and buttons. Theseand other UI elements can be related to or represent the functions ofthe web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, for example, as a data server, or that includes a middlewarecomponent, for example, an application server, or that includes afront-end component, for example, a client computer having a graphicaluser interface or a Web browser through which a user can interact withan implementation of the subject matter described in this specification,or any combination of one or more such back-end, middleware, orfront-end components. The components of the system can be interconnectedby any form or medium of wireline or wireless digital data communication(or a combination of data communication), for example, a communicationnetwork. Examples of communication networks include a local area network(LAN), a radio access network (RAN), a metropolitan area network (MAN),a wide area network (WAN), Worldwide Interoperability for MicrowaveAccess (WIMAX), a wireless local area network (WLAN) using, for example,802.11 a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 orother protocols consistent with the present disclosure), all or aportion of the Internet, another communication network, or a combinationof communication networks. The communication network can communicatewith, for example, Internet Protocol (IP) packets, Frame Relay frames,Asynchronous Transfer Mode (ATM) cells, voice, video, data, or otherinformation between network nodes.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what can be claimed, but rather asdescriptions of features that can be specific to particularimplementations of particular inventions. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented, in combination, in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations, separately, or in any sub-combination. Moreover,although previously described features can be described as acting incertain combinations and even initially claimed as such, one or morefeatures from a claimed combination can, in some cases, be excised fromthe combination, and the claimed combination can be directed to asub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. While operations are depicted inthe drawings or claims in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed (some operations can be considered optional), toachieve desirable results. In certain circumstances, multitasking orparallel processing (or a combination of multitasking and parallelprocessing) can be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules andcomponents in the previously described implementations should not beunderstood as requiring such separation or integration in allimplementations, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Accordingly, the previously described example implementations do notdefine or constrain the present disclosure. Other changes,substitutions, and alterations are also possible without departing fromthe spirit and scope of the present disclosure.

Furthermore, any claimed implementation is considered to be applicableto at least a computer-implemented method; a non-transitory,computer-readable medium storing computer-readable instructions toperform the computer-implemented method; and a computer systemcomprising a computer memory interoperably coupled with a hardwareprocessor configured to perform the computer-implemented method or theinstructions stored on the non-transitory, computer-readable medium.

What is claimed is:
 1. A computer-implemented method, comprising:acquiring, by data processing apparatus, for each service scenario of aplurality of service scenarios, respective representative features of aplurality of users who have accessed the service scenario, wherein eachservice scenario is a different respective access interface foraccessing a different respective service of a website; obtaining, by thedata processing apparatus, features of a to-be-matched product to bepresented to users accessing one of the service scenarios; calculating,by the data processing apparatus, a respective user feature mappingvalue of each service scenario of the plurality of the service scenariosbased on matching the features of the to-be-matched productcorresponding to the service scenario to respective representativefeatures of the plurality of the users who have accessed the servicescenario, comprising: generating a quantified first user feature byquantifying a first user feature corresponding to a first matchingfeature of the to-be-matched product corresponding to each servicescenario according to a quantification rule, wherein the quantificationrule defines a range within which the quantified first user featurefalls; and calculating the respective user feature mapping value of eachservice scenario based on the quantified first user featurecorresponding to the first matching feature according to a mapping rule,wherein the mapping rule comprises one or more functions of thequantified first user feature corresponding to the first matchingfeature; selecting, by the data processing apparatus, among theplurality of the service scenarios, a target service scenario for theto-be-matched product based on the respective user feature mapping valueof the target service scenario; and providing, by the data processingapparatus, the to-be-matched product in the selected target servicescenario, wherein the selected target service scenario is an accessinterface for accessing a particular service of the website.
 2. Thecomputer-implemented method of claim 1, wherein the respectiverepresentative features of the plurality of the users who have accessedeach service scenario comprises features derived from registrationinformation or an access record of the plurality of the users who haveaccessed each service scenario.
 3. The computer-implemented method ofclaim 1, wherein the quantification rule is the same for the pluralityof the service scenarios, and wherein the mapping rule is the same forthe plurality of the service scenarios.
 4. The computer-implementedmethod of claim 1, wherein selecting, among the plurality of the servicescenarios, the target service scenario for the to-be-matched productbased on the respective user feature mapping value of the target servicescenario comprises: selecting a service scenario having a lowest userfeature mapping value among a plurality of respective user featuremapping values of the plurality of the service scenarios as the targetservice scenario for the to-be-matched product; or selecting a servicescenario having a highest user feature mapping value among a pluralityof respective user feature mapping values of the plurality of theservice scenarios as the target service scenario for the to-be-matchedproduct.
 5. The computer-implemented method of claim 1, whereinobtaining features of the to-be-matched product to be presented to theusers accessing one of the service scenarios comprises acquiring thematching features of the to-be-matched product corresponding to one ofthe service scenarios based on the respective representative features ofthe plurality of the users who have accessed the service scenariosaccording to a machine learning algorithm.
 6. The computer-implementedmethod of claim 5, wherein the machine learning algorithm comprises atleast one or more of a logistic regression algorithm, a GradientBoosting Decision Tree (GBDT) algorithm, a decision tree algorithm, or adeep learning algorithm.
 7. The computer-implemented method of claim 1,wherein calculating the respective user feature mapping value of eachservice scenario based on the matching features of the to-be-matchedproduct corresponding to each service scenario comprises: obtaining amapping function between the respective user feature mapping value ofeach service scenario and the matching features of the to-be-matchedproduct corresponding to each service scenario using a machine learningmethod; and calculating the respective user feature mapping value ofeach service scenario based on the mapping function.
 8. Anon-transitory, computer-readable medium storing one or moreinstructions executable by a computer system to perform operationscomprising: acquiring, by data processing apparatus, for each servicescenario of a plurality of service scenarios, respective representativefeatures of a plurality of users who have accessed the service scenario,wherein each service scenario is a different respective access interfacefor accessing a different respective service of a web site; obtaining,by the data processing apparatus, features of a to-be-matched product tobe presented to users accessing one of the service scenarios;calculating, by the data processing apparatus, a respective user featuremapping value of each service scenario of the plurality of the servicescenarios based on matching the features of the to-be-matched productcorresponding to the service scenario to respective representativefeatures of the plurality of the users who have accessed the servicescenario, comprising: generating a quantified first user feature byquantifying a first user feature corresponding to a first matchingfeature of the to-be-matched product corresponding to each servicescenario according to a quantification rule, wherein the quantificationrule defines a range within which the quantified first user featurefalls; and calculating the respective user feature mapping value of eachservice scenario based on the quantified first user featurecorresponding to the first matching feature according to a mapping rule,wherein the mapping rule comprises one or more functions of thequantified first user feature corresponding to the first matchingfeature; selecting, by the data processing apparatus, among theplurality of the service scenarios, a target service scenario for theto-be-matched product based on the respective user feature mapping valueof the target service scenario; and providing, by the data processingapparatus, the to-be-matched product in the selected target servicescenario, wherein the selected target service scenario is an accessinterface for accessing a particular service of the website.
 9. Thecomputer-readable medium of claim 8, wherein the respectiverepresentative features of the plurality of the users who have accessedeach service scenario comprises features derived from registrationinformation or an access record of the plurality of the users who haveaccessed each service scenario.
 10. The computer-readable medium ofclaim 8, wherein the quantification rule is the same for the pluralityof the service scenarios, and wherein the mapping rule is the same forthe plurality of the service scenarios.
 11. The computer-readable mediumof claim 8, wherein selecting, among the plurality of the servicescenarios, the target service scenario for the to-be-matched productbased on the respective user feature mapping value of the target servicescenario comprises: selecting a service scenario having a lowest userfeature mapping value among a plurality of respective user featuremapping values of the plurality of the service scenarios as the targetservice scenario for the to-be-matched product; or selecting a servicescenario having a highest user feature mapping value among a pluralityof respective user feature mapping values of the plurality of theservice scenarios as the target service scenario for the to-be-matchedproduct.
 12. The computer-readable medium of claim 8, wherein obtainingfeatures of the to-be-matched product to be presented to the usersaccessing one of the service scenarios comprises acquiring the matchingfeatures of the to-be-matched product corresponding to one of theservice scenarios based on the respective representative features of theplurality of the users who have accessed the service scenarios accordingto a machine learning algorithm.
 13. The computer-readable medium ofclaim 12, wherein the machine learning algorithm comprises at least oneor more of a logistic regression algorithm, a Gradient Boosting DecisionTree (GBDT) algorithm, a decision tree algorithm, or a deep learningalgorithm.
 14. The computer-readable medium of claim 8, whereincalculating the respective user feature mapping value of each servicescenario based on the matching features of the to-be-matched productcorresponding to each service scenario comprises: obtaining a mappingfunction between the respective user feature mapping value of eachservice scenario and the matching features of the to-be-matched productcorresponding to each service scenario using a machine learning method;and calculating the respective user feature mapping value of eachservice scenario based on the mapping function.
 15. Acomputer-implemented system, comprising: one or more computers; and oneor more computer memory devices interoperably coupled with the one ormore computers and having tangible, non-transitory, machine-readablemedia storing one or more instructions that, when executed by the one ormore computers, perform one or more operations comprising: acquiring, bydata processing apparatus, for each service scenario of a plurality ofservice scenarios, respective representative features of a plurality ofusers who have accessed the service scenario, wherein each servicescenario is a different respective access interface for accessing adifferent respective service of a web site; obtaining, by the dataprocessing apparatus, features of a to-be-matched product to bepresented to users accessing one of the service scenarios; calculating,by the data processing apparatus, a respective user feature mappingvalue of each service scenario of the plurality of the service scenariosbased on matching the features of the to-be-matched productcorresponding to the service scenario to respective representativefeatures of the plurality of the users who have accessed the servicescenario, comprising: generating a quantified first user feature byquantifying a first user feature corresponding to a first matchingfeature of the to-be-matched product corresponding to each servicescenario according to a quantification rule, wherein the quantificationrule defines a range within which the quantified first user featurefalls; and calculating the respective user feature mapping value of eachservice scenario based on the quantified first user featurecorresponding to the first matching feature according to a mapping rule,wherein the mapping rule comprises one or more functions of thequantified first user feature corresponding to the first matchingfeature; selecting, by the data processing apparatus, among theplurality of the service scenarios, a target service scenario for theto-be-matched product based on the respective user feature mapping valueof the target service scenario; and providing, by the data processingapparatus, the to-be-matched product in the selected target servicescenario, wherein the selected target service scenario is an accessinterface for accessing a particular service of the website.
 16. Thecomputer-implemented system of claim 15, wherein the respectiverepresentative features of the plurality of the users who have accessedeach service scenario comprises features derived from registrationinformation or an access record of the plurality of the users who haveaccessed each service scenario.
 17. The computer-implemented system ofclaim 15, wherein the quantification rule is the same for the pluralityof the service scenarios, and wherein the mapping rule is the same forthe plurality of the service scenarios.